Kavli Affiliate: Kristin A. Persson
| First 5 Authors: Ilyes Batatia, Ilyes Batatia, , ,
| Summary:
Atomistic simulations of matter, especially those that leverage
first-principles (ab initio) electronic structure theory, provide a microscopic
view of the world, underpinning much of our understanding of chemistry and
materials science. Over the last decade or so, machine-learned force fields
have transformed atomistic modeling by enabling simulations of ab initio
quality over unprecedented time and length scales. However, early ML force
fields have largely been limited by: (i) the substantial computational and
human effort of developing and validating potentials for each particular system
of interest; and (ii) a general lack of transferability from one chemical
system to the next. Here we show that it is possible to create a
general-purpose atomistic ML model, trained on a public dataset of moderate
size, that is capable of running stable molecular dynamics for a wide range of
molecules and materials. We demonstrate the power of the MACE-MP-0 model – and
its qualitative and at times quantitative accuracy – on a diverse set of
problems in the physical sciences, including properties of solids, liquids,
gases, chemical reactions, interfaces and even the dynamics of a small protein.
The model can be applied out of the box as a starting or "foundation" model for
any atomistic system of interest and, when desired, can be fine-tuned on just a
handful of application-specific data points to reach ab initio accuracy.
Establishing that a stable force-field model can cover almost all materials
changes atomistic modeling in a fundamental way: experienced users get reliable
results much faster, and beginners face a lower barrier to entry. Foundation
models thus represent a step towards democratising the revolution in
atomic-scale modeling that has been brought about by ML force fields.
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